# Predicting Customer Satisfaction by Replicating the Survey Response Distribution

arXiv: https://arxiv.org/abs/2411.12539 · HTML: https://mlsanity.com/paper/2411.12539/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)

## Abstract (arXiv)

For many call centers, customer satisfaction (CSAT) is a key performance indicator (KPI). However, only a fraction of customers take the CSAT survey after the call, leading to a biased and inaccurate average CSAT value, and missed opportunities for coaching, follow-up, and rectification. Therefore, call centers can benefit from a model predicting customer satisfaction on calls where the customer did not complete the survey. Given that CSAT is a closely monitored KPI, it is critical to minimize any bias in the average predicted CSAT (pCSAT). In this paper, we introduce a method such that predicted CSAT (pCSAT) scores accurately replicate the distribution of survey CSAT responses for every call center with sufficient data in a live production environment. The method can be applied to many multiclass classification problems to improve the class balance and minimize its changes upon model updates.